1. Technical Field
The present invention generally relates to signal processing at communication base stations, and particularly relates to differentiated linear equalization of user signals at such base stations.
2. Background Information
In certain types of wireless communication networks, the received signal at a given network base station comprises a received composite signal that includes signals of interest from a plurality of mobile terminals (“users”) being supported by the base station. As one example, many users in a Code Division Multiple Access (CDMA) network may simultaneously transmit on the uplink to a supporting base station. That base station receives all of these signals of interest together as a composite, along with any number of interfering signals, and recovers each individual signal of interest by, for example, correlating the composite signal with the unique uplink scrambling code of each user. Similarly, in the downlink, a mobile terminal receives signals transmitted simultaneously from a plurality of multiple base stations.
Indeed, a common aspect of such processing is the correlation of the received composite signal with each user's (or base station's) scrambling code at different code (delay) offsets, to obtain multipath versions of each user's signal of interest. As is well known, these multipath versions can be combined to obtain signal-to-noise ratio (SNR) improvements. In a basic combining system, such as in the well known “Rake” receiver architecture, each signal of interest is despread by a plurality of Rake “fingers” positioned at delay offsets corresponding to the (primary) multipath propagation delays of the signal. A combining circuit then combines the finger output signals using combining weights determined from the complex channel coefficient estimated for each delay path.
Rake processing in the above manner yields SNR improvements for each signal of interest in AWGN conditions, i.e., in the absence of colored interference bearing on the signals of interest. Where spectrally biased interference is at play, which is a common phenomenon in existing and developing wireless communication networks, more sophisticated combining weights are needed to provide “whitening” of the combined signal. To this end, linear equalization receivers, such as “Generalized Rake” (G-Rake) receivers and chip equalizer (CE) receivers, use combining weights that consider the affects of colored interference. However, the computation of these more sophisticated combining weights is not trivial, and generally involves potentially burdensome computations arising from the generation of correlation estimates for each signal of interest. These correlation estimates provide the basis for the computation of whitening combining weights.
In more detail, the received composite signal at a CDMA base station consists of a number of desired signals from users in the base station's own coverage area (cell/sectors), and a number of interfering signals from users in other cells. The other-cell interference may include high-rate, high-power signals, which may arise, for example, from a lack of user transmission scheduling coordination between cells. The presence of such high-power interfering signals will often result in considerable performance degradation to the signals of interest. Thus to improve system capacity and stability, it is desirable to suppress such high-power other-cell interfering signals.
As noted above, linear equalization receiver structures are effective in suppressing dominating colored interference. When multiple receive antennas are available, multiple dominating interfering signals can be suppressed. As is known, combining weights of G-Rake and CE receivers can be derived based on a Minimum Mean Square Error (MMSE) formulation or a Maximum Likelihood (ML) formulation. Also, signal quality can be estimated.
According to the MMSE formulation, the combining weights arewMMSE=Rd−1h,  Eq. (1)where Rd is a matrix of received signal sample correlations or pilot/data symbol despread value correlations, and h is the net channel response. Signal quality can be estimated as
  SINR  =                              w          MMSE          H                ⁢        h                    1        -                              w            MMSE            H                    ⁢          h                      =                                        h            H                    ⁢                      R            d                          -              1                                ⁢          h                          1          -                                    h              H                        ⁢                          R              d                              -                1                                      ⁢            h                              .      The ML formulation has combining weights given aswML=Ru−1h,  Eq. (2)where Ru is the impairment covariance matrix. Signal quality can be estimated asSINR=wMLHh=hHRu−1h. For the G-Rake receiver structure, the matrix elements of Rd and Ru are functions of the differences between G-Rake “finger” delays. Equivalently, for the CE receiver structure, the elements of these matrices are a function of the differences between equalization filter tap delays. The elements may also be a function of the sampling phase. In general, delay differences are associated with receive antennas and sampling phases. Thus, when discussing same delay differences, it implies same sampling phases as well.
More efficient G-Rake receiver processing has been proposed, based on the realization that multiple users of interest may share the same finger delay differences. These teachings propose processing the composite received (uplink) signal at the base station, which includes potentially many signals of interest from a plurality of users being supported by the base station. Particularly, the proposed processing calculates received signal sample correlations for a set of delay differences. For G-Rake, the set of delay differences includes the unique relative delay differences for the signals of interest included in the received composite signal. In this manner, the received signal sample correlations can be shared by users having the same relative finger delay differences. The same sharing methodology applies to CE receiver realizations, where more than one user may share the same equalization filter tap delay differences.
Another proposed approach to obtaining shared correlations for use with more than one user's uplink signal of interest relies on the fact that there commonly are many unused codes for users in the uplink. That is, in the CDMA uplink, each user's uplink transmissions are covered by a different scrambling code, meaning that the underlying spreading code set is distinguished between users, and most individual users do not come close to exhausting the full set of spreading codes available in the set. Thus, for any given user, there are one or more unused uplink codes available for estimating impairment correlations.
Particularly, an impairment covariance matrix can be directly estimated using the unused codes of a given user, and this matrix can then be used to generate an estimated data covariance matrix for sharing among multiple users. One approach forms the estimate as{tilde over (R)}d≈{circumflex over (R)}u,UOI+ĥUOIĥUOIH,  Eq. (3)where ĥUOI and {circumflex over (R)}u,UoI are respectively the estimated net response and the estimated impairment covariance matrix estimated using the unused codes for a first user of interest (UOI). The data covariance matrix {tilde over (R)}d is shared among the receivers at the base station for processing the signals of interest for other users.
However, while the use of shared correlations offers processing efficiencies, the approach may not provide acceptable performance for processing uplink signals received from certain users. More broadly, the use of shared correlations may not provide acceptable performance where the signal of interest is a relatively high power signal, e.g., where it is a dominant component in the composite received signal. In such cases, the received signal sample correlations are of the form{circumflex over (R)}d=Ru+hhH,  Eq. (4)where hhH is a significant portion of {circumflex over (R)}d. (The “H” denotes the Hermitian matrix.) With the estimated net response ĥ, the combining weights used for Rake combining becomeŵMMSE={circumflex over (R)}d−1ĥ.  Eq. (5)
Note that the combining weights in Eq. (5) are determined jointly based on the true net response embodied in {circumflex over (R)}d and the estimated net response ĥ. This circumstance creates a mismatch problem that becomes significant when hhH dominates the received signal sample correlation matrix and the net response estimate is noisy.
For example, FIG. 1 is a graph illustrating simulated performance for a hypothetical practical G-Rake receiver versus an idealized receiver, for a given signal of interest that is a high-rate/high-power signal. The performance is shown for different amounts of sample averaging for the practical receiver in comparison with the performance of the idealized receiver, where the idealized receiver maintains ideal MMSE combining weights for a given signal of interest. One sees that no matter how much averaging is used to get received signal sample correlations, the raw Bit Error Rate (BER) deviates significantly from the idealized receiver when shared correlation estimates are used for the signal of interest.